Summary "Introduction to Machine Learning for Absolute Beginners"
2 views 0 purchase
Course
Machine Learning
Institution
Machine Learning
The document is a summary of a lecture tutorial on machine learning for beginners. The tutorial is conducted by Kylie Ying, a physicist and engineer who has worked at MIT, CERN, and Free Code Camp. The tutorial covers the basics of supervised and unsupervised learning models, programming machine le...
Machine learning for everybody
In this lecture , we 'll talk about supervised and unsupervised learning
models. We 'll also see how we can program it on Google CoLab. In
order to import the data set, NumPy will import NumPy, pandas and
matplotlib. And then we 'll import other things as we go. So basically,
this command here just reads some CSV file that you pass in CSV has
come about comma separated values. So now if I pass in a names here
, then it basically assigns these labels to the columns of this data set.
So I 'm just going to take these values and make that the column
names. We 're going to use this example to try to predict for future
samples. Each of these samples have one quality for each or one value
for each of these labels up here. And that is something known as
classification. So for you know , sample zero, I have 10 different
features. So I have. 10 different values that I can pass into some
model. And I can spit out.
Machine learning is a sub domain of computer science that focuses on
certain algorithms that might help a computer learn from data without
a programmer telling the computer exactly what to do. That 's what we
call explicit programming. So AI is artificial intelligence. Data science
is a field that attempts to find patterns and draw insights from data.
All of these fields kind of overlap and all of them might use machine
learning. A machine learning model looks like you have a bunch of
inputs that are going into some model. And then the model is spitting
out an output , which is our prediction. In both of these, there 's no
inherent order built into either of these categorical data sets. That 's
not like we can rate us one and France to Japan three , etc. One hot
encoding is saying if it matches some category, make that a one. And
if it does n't just make that zero, that's one hot encoding. There are
also a different type of qualitative feature. These are known as ordinal
pieces of data because they have some sort of inherent order. So for
these types of data sets we can just mark them from one to five.
There are some different tasks in supervised learning that there are
different tasks. In supervised learning, there is one classification, and
basically classification. But there is also binary classification. And
binary classification, you might have hot dog , or not hot dog. So
there's only two categories that you 're working with something that is
something and something that's is n't binary. Each row is a different
sample in the data. So each row of this will be fed into our model. And
our model will make some sort of prediction. And what we do is we
compare that prediction to the actual value of y. So I 've condensed
this to a chocolate bar to kind of talk about some of the other concepts
in machine learning. We break up our whole data set that we have into
three different types of data sets. We call it the training data set, the
validation data set and the testing data set. The validation set is kind
of used as a reality check during or after training to ensure that the
model can handle unseen data still.
The benefits of buying summaries with Stuvia:
Guaranteed quality through customer reviews
Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.
Quick and easy check-out
You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.
Focus on what matters
Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!
Frequently asked questions
What do I get when I buy this document?
You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.
Satisfaction guarantee: how does it work?
Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.
Who am I buying these notes from?
Stuvia is a marketplace, so you are not buying this document from us, but from seller harshrajbangre. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for $7.99. You're not tied to anything after your purchase.
